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AI Research
arXiv CS.AI·Jun 10·5 min read
Geometrically Averaged Hard Target Updates for Linear Q-Learning
TL;DR
In Q-learning, stability issues arise from the frequent updates of target values. A new method called the λ-target update is introduced, which averages periodic target updates using geometric weights.
✦ Why It Matters
Engineers can implement λ-target updates to enhance the stability of linear Q-learning applications.
Key Takeaways
- Explore λ-target updates for improved Q-learning stability.
- Utilize geometric weights for averaging target updates.
- Apply findings to both deterministic and stochastic environments.
- Consider implications for real-world reinforcement learning applications.
Full Summary
Q-learning, a popular reinforcement learning technique, often struggles with stability due to hard target updates. To address this, a novel method called the λ-target update is proposed, which utilizes geometric weights to average periodic target updates.
Specifically, the weights are defined as (1-λ)λ^(m-1), where λ ranges from 0 to 1, allowing for a smooth transition between different update strategies. The study focuses on linear Q-learning, employing a switching-system model to analyze the effectiveness of this approach.
Results indicate that the λ-target update significantly improves stability compared to traditional methods, particularly in deterministic settings, while also being extendable to stochastic environments. This advancement could lead to more robust applications of Q-learning in various AI tasks.